Optimizing CHD Prevention Using the Electronic Health Record

Transcription

1 Optimizing CHD Prevention Using the Electronic Health Record Project Lead: Stacey Sheridan, MD, MPH Team Members: Arlene Chung, MD, MHA, MMCi (NC-TRACS); Sam Cykert, MD (NC-Regional Extension Center); Annie Whitney, MS (QI); Ashwani Kaul (ISD) SPECIFIC AIMS Coronary heart disease (CHD) is one of the leading causes of morbidity and mortality in the US and the focus of multiple national campaigns to reduce its impact. An important part of primary CHD prevention is calculating global CHD risk (i.e. a quantitative estimate of a patient s chances of CHD events calculated by combining risk factors in an empirical equation). CHD risk calculation has been both recommended by national guidelines and linked to improved clinical outcomes when given to patients along with appropriate medication and counseling on risk reduction and adherence. However, it is underused in clinical practice. The current national focus on implementation and meaningful use of electronic health records (EHRs) offers an important opportunity for increasing CHD risk calculation and improving prevention. Not only is EHR implementation and Meaningful Use a focus of providers time over the next few years, but meaningful use of the EHR necessitates meeting both functionality and quality targets that would support improved CHD care. In this application, we propose to develop and test an EHR-based prevention program to improve both CHD risk calculation and CHD prevention outcomes. The development will augment ongoing development of disease registries in UNC s Clinical Information System, but will introduce innovative interventions and strategically plan for the transition of data, decision rules, and care processes when UNC implements Epic Information Systems in Specific aims are listed below. Aim 1: Develop an EHR-based CHD prevention program to promote CHD risk calculation, appropriate prescribing, and patient adherence to prescribed medications. Aim 2: Perform iterative testing of the accuracy of the EHR program in assessing CHD risk and generating clinical prompts. Aim 3: Test the usability of the EHR program. Aim 4: Use pre-post analysis to assess the effect of the intervention on CHD risk assessment and guideline concordant prescribing of CHD prevention medications. BACKGROUND AND SIGNIFICANCE Coronary heart disease (CHD) is responsible for one-third of deaths in the United States and is one of the leading causes of premature, permanent disability. 1 This high medical and financial burden has made CHD a critical public health issue for the United States and prompted national campaigns to improve prevention of CHD. 2 Professional agencies recommend preventing CHD by preventing the development of CHD risk factors through healthy diet and exercise (primordial prevention) and by treating elevated risk factors (e.g. hypertension, abnormal lipids, and smoking) in those without known CHD (primary prevention) and in those with it (secondary prevention). Treatment of elevated CHD risk factors has accounted for 50% of the total decrease in CHD death over the last 30 years. 3-5 Additionally, treatment of elevated risk factors in those without known CHD (primary prevention) has decreased CHD incidence. 5 Primary prevention is, therefore, a particularly cost-effective strategy in reducing CHD death. 4 An important part of primary CHD prevention is calculating global CHD risk (i.e. a quantitative estimate of a patient s chances of CHD events calculated by combining risk factors in an empirical

2 equation). Guidelines now emphasize the importance of global CHD risk as a starting point when considering aspirin and cholesterol medication for the primary prevention of CHD. 6,7 Further calculation of global CHD risk has been linked both to appropriate prescribing of cardiovascular drugs and to improved patient outcomes (including reduced CHD risk) when it is provided to patients in conjunction with counseling on risk reduction and adherence. 8,9 Despite evidence that CHD risk calculation is beneficial, calculation of global CHD risk in practice remains low. Several national or multi-national studies suggest that rates of CHD risk calculation are around or below 50%, making global risk calculation a missed opportunity for improved CHD care. Time is the most commonly cited barrier to risk calculation. Other important barriers include lack of available risk calculators in clinical practice and lack of knowledge about how to apply risk calculation The current national focus on implementation and meaningful use of electronic health records (EHRs) offers an important opportunity for both increasing CHD risk calculation and improving CHD prevention. Not only is EHR implementation a focus of providers time over the next few years, but also meaningful use of the EHR necessitates meeting both functionality and quality targets that might improve CHD prevention outcomes These factors make EHR implementation a logical focus of quality improvement efforts for CHD prevention. PROJECT GOALS, INCLUDING THEIR RELATION TO THE GOALS OF UNC HEATH CARE In this application, we propose to develop and test an EHR-based CHD prevention program. This program will augment ongoing development of disease registries in UNC s Clinical Information System, but will introduce innovative and disruptive interventions (i.e. CHD risk calculation) and strategically plan for the transition of data, decision rules, and care processes when UNC implements Epic Information Systems in PRELIMINARY STUDIES Primary CHD Prevention in the UNC General Medicine Clinic: To assess prescribing of CHD prevention medications in the UNC general medicine clinic, two investigators (Dr. Sheridan and Ms. Marsil) independently reviewed a random sample of 197 charts from new patient visits with 79 different physicians between June 2002 and June The review showed that overall prescribing rates of hypertension medications for those with hypertension (47%), cholesterol medications for those with abnormal cholesterol (22%), and aspirin for those with a 10-year predicted CHD risk of >10% (32%) were low, as was documentation of calculation of CHD risk (3%). A Hypertension Registry for UNC s Primary Care Collaborative: Addressing low rates of hypertension treatment, the primary care collaborative at UNC developed a hypertension registry with input from experts in family medicine (Dr. Viera), general medicine (Dr. Sheridan), cardiology (Dr. Hinderliter), and quality improvement (Annie Whitney/Robb Malone). This registry draws data from UNC s clinical information system, claims data, and health surveys (via UNC s data warehouse) to assess individual s blood pressure, comorbidities, and risk for future CHD events. Collected information and calculated risk for future events is displayed in a dashboard embedded in the clinical information system. A rules engine is used to generate and display prompts to staff for action at the patient's next visit. Currently, prompts nudge only ordering of needed lab tests; however, future prompts are planned to nudge referrals to appropriate lifestyle counseling and initiation or intensification of pharmacotherapy..following principals of good development, 15,16 prompts are displayed on a single page to reduce provider fatigue. Additionally, to maximize clinical outcomes, 17 the registry has been integrated into the care process at a test site (Carolina Advanced Health, an innovative UNC practice). Further, it facilitates population level management (through displays of interactive patient lists in the EHR) and reporting to support quality measures.

3 A Patient-Directed Intervention to Improve Use and Adherence to CHD Prevention: To address patient barriers to CHD prevention, Dr. Sheridan and colleagues have conducted numerous trials of a web-based decision aid and tailored messaging system to encourage use and adherence to CHD prevention The details of the most recent trial are described in the table below. Study (Funder, Dates) Heart to Health Trial (CDC, 2011 to 2013) Study Design and Setting RCT (web vs. counselor version) 5 practices in North Carolina Family Medicine Research Network Intervention Focus Diet, Physical activity, Smoking Cessation, Medications Intervention Content and Delivery Brief decision aid (education on CHD risk and treatment options and advice to think about one s values for treatment ) + Enhanced tailored messages addressing barriers to lifestyle change and adherence and facilitating creation of action plans +Maintenance messages on relapse prevention, problem solving, and lessons for long-term maintenance + single provider education session Delivered separate from clinician visit Study Population 490 men and women at high risk of CHD; 75% white, 25% black; 14% low literacy; 67% with low or moderate baseline adherence to CHD meds on Morisky 8 scale Major Findings At immediate follow-up: Intent to change lifestyle: 67% Intent to take medications: 33% At 4-month follow-up, counselor versus web: 10-year predicted CHD risk: -0.8 percentage points, p 0.04 (but, no difference in counselor versus web if chose meds) Cost-effectiveness: web arm more incrementally cost-effective than counselor arm per 1% risk reduction Acceptability: Patients find web and counselor arms equally acceptable. Providers think web more feasible due to staffing issues related to counseling. At 4-month follow-up, within web-arm: Predicted CHD risk: -1.4 percentage points, p <0.001 (-2.8 percentage points if chose meds) % high adherence (Morisky scale): +20%, p< SBP: -1.1 mmhg (-15.4 mmhg if chose BP meds) Total cholesterol: -3.8 mg/dl (-25.2 mg/dl if chose statin) HDL-c: +1.8 mg/dl Aspirin use: +11% (confirmed by thromboxane b2) Retention rate: 92%. APPROACH AND METHODS Methods Overview: We plan to conduct a series of interrelated activities in UNC s General Medicine Clinic that will support the development and testing of a comprehensive EHR-based CHD prevention program for UNC s primary care clinics. Activities will include 1) development of a practical EHR-based program to promote CHD risk calculation, appropriate prescribing, and patient adherence to prescribed medications (and ultimately reduced risk factors and CHD risk), 2) iterative testing of the accuracy of developed EHR-based calculations and prompts, 3) testing the usability of the EHR-based program, and 4) pilot testing the effectiveness of the EHR-based system on clinical outcomes. UNC General Medicine Clinic and Its EHR: The UNC general medicine clinic, where we plan to conduct our work, provides primary care health services for 12,000 patients, including vulnerable patients who rely on the practice because of lack of financial access to other high-quality care. The clinic has a culture of continuous quality improvement (QI) with a dedicated QI Coordinator and multiple ongoing QI projects (e.g. CRC screening, depression screening/management) conducted by staff and residents. Like the rest of UNC Hospitals, the general medicine clinic conducts business using the homegrown EHR Web-CIS. This system catalogs patients problems, medications, allergies, visits, tests, and hospitalizations and has been programmed for electronic prescribing, inpatient order entry, and prompting to encourage preventive care and disease management. In 2014, UNC hospitals (including the general medicine clinic) will transfer from Web-CIS to the Epic EHR.

4 Development of the Intervention: The practical EHR-based intervention we propose to develop combines features of the Web-CIS-based hypertension registry we ve developed with innovations inspired by our work in CHD adherence. It will include the underlined components shown below. Automated CHD risk calculation: For patients with no known history of cardiovascular disease, we will calculate the predicted likelihood of future CHD events over 10 years (i.e. predicted CHD risk) using a well-validated continuous Framingham risk equation. 21 To facilitate aspirin decisions in women, we will also calculate the predicted likelihood of stroke. 22,23 In order to make these calculations, the system will access vital, lab, demographic and registry data. Provider prompts for prescribing and monitoring: We will use an external rules engine to generate physician prompts. Aspirin prompts will be generated for men with predicted CHD risk >10% and no contraindications to aspirin and for women with a predicted stroke risk >10% and no contraindications. Cholesterol medication prompts (specifically for statins) will be generated for men and women with CHD risk >10% and no contraindications to statins. Prompts for generic prescribing, which is known to improve medication adherence (and ultimately clinical outcomes), will be issued 1) with prompts for new prescriptions and 2) when trade name statins are listed in the medication lists. Patient adherence support: When new medications are prescribed, we will support patient adherence (and thus reduction of risk factors and achievement of meaningful use targets) by pre-populating clinical visit summaries with 1) cholesterol data and CHD risk levels that can be used for self-monitoring and 2) with adherence messages that we have previously designed and tested. We will further support adherence by 3) providing information about accessing our web-based decision aid and tailored messaging system (described under preliminary studies); we envision that this system could ultimately be hosted in the patient portal. Population level reports: To allow population management of CHD risk, we will generate reports of patients at high risk and due for medications or monitoring. To maximize intervention impact, we will design our intervention to address the complex interplay of provider, patient, tool, task, and organizational factors as described by the Human Factors Engineering Model. 16,24 Specifically, we will ensure good EHR design, integration into existing processes, and adequate provider training and support during implementation. Further, we will ensure that our team has full training in the forthcoming EPIC information system so that developed decision rules and care processes can be seamlessly transferred to this system in We will additionally monitor accuracy and impact as described below. Testing the Intervention for Accuracy of Risk Assessment and Prompts: To determine the accuracy of automated risk assessments and prompts in our intervention, we will compare these risk assessments and prompts with the risk assessments and recommendations generated from manual review of chart data. To conduct this analysis, we will randomly sample and review 50 charts. To identify charts, we will query the UNC Administrative Database for a list of new and return visits between January and March We will then use a computer generated random number list to select a random sample of eligible charts for review. Charts will be eligible if they are for men and women age who have an established provider in the general medicine clinic and no history of cardiovascular disease or serious illness that would limit life expectancy to less than 2 years. A single reviewer will review charts to abstract information on cardiovascular risk factors (age; gender; systolic blood pressure; total, HDL, and LDL cholesterol; smoking and diabetes status; left ventricular hypertrophy; and atrial fibrillation) and prescribed cardiovascular medications (including generic versus trade prescriptions). A second reviewer will independently review a random sample of 10% of charts to assure process validity. Reviewers will then calculate CHD risk (and stroke risk for women) and determine recommended care using pre-

5 determined algorithms. They will compare their assessments with automated assessments and catalog any inaccuracies in the automated process. Based on results, the research team will revise intervention programming as necessary to achieve acceptable accuracy. Testing the Usability of the Intervention: To understand the usability of the EHR-based intervention, we will recruit 6-8 clinicians and staff to participate in usability testing. Participants will be asked to think aloud as they proceed through each newly developed EHR screen and to give feedback on both the interface and content with regard to ease of use, clarity, and positioning to aid workflow. Based on results, the research team will revise the intervention as necessary to achieve acceptable usability. Testing the Effectiveness and Acceptability of the Intervention: To assess the effect of the intervention in UNC s General Medicine, we will conduct assessment of key outcomes 1 week before and 1 month after intervention implementation. To perform this analysis, we will purposely select a consecutive sample of 50 high risk patients (as designated by automated risk assessment) both pre and post intervention implementation. Key outcomes will be assessed by EHR-generated reports and will include: the number of high risk patients who are appropriately treated with aspirin and cholesterol medications (based on criteria described above); and the proportion of individuals receiving generic medications. PROJECT MONITORING: To ensure project success, we will create a detailed schedule at the beginning of the project period and generate weekly reports on progress toward stated development and recruitment goals. TIMELINE: Task Quarter 1 Quarter 2 Quarter 3 Quarter 4 Develop EHR intervention Test intervention accuracy Test intervention usability, acceptability, and effectiveness SUSTAINABILITY AND SCALABILITY: As noted above, this work will be conducted with an eye toward the forthcoming EPIC transition to ensure seamless transfer of data, decision rules, and care processes. If successful, this work will be scaled to other primary and specialty clinics affiliated with UNC and to Epic users throughout the state of North Carolina via the NC Regional Extension Center. Literature Cited 1. Lloyd-Jones D, Adams RJ, Brown TM, et al. Heart disease and stroke statistics update: a report from the American Heart Association. Circulation Feb ;121(7):e46-e Centers for Disease C, Prevention. Million hearts: strategies to reduce the prevalence of leading cardiovascular disease risk factors--united States, MMWR Morb Mortal Wkly Rep. Sep ;60(36): Ford ES, Ajani UA, Croft JB, et al. Explaining the decrease in U.S. deaths from coronary disease, N Engl J Med Jun ;356(23): Goldman L, Phillips KA, Coxson P, et al. The effect of risk factor reductions between 1981 and 1990 on coronary heart disease incidence, prevalence, mortality and cost. J Am Coll Cardiol Oct 2001;38(4):

6 5. Hunink MG, Goldman L, Tosteson AN, et al. The recent decline in mortality from coronary heart disease, The effect of secular trends in risk factors and treatment. JAMA Feb ;277(7): Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA May ;285(19): Pearson TA, Blair SN, Daniels SR, et al. AHA Guidelines for Primary Prevention of Cardiovascular Disease and Stroke: 2002 Update: Consensus Panel Guide to Comprehensive Risk Reduction for Adult Patients Without Coronary or Other Atherosclerotic Vascular Diseases. American Heart Association Science Advisory and Coordinating Committee. Circulation Jul ;106(3): Sheridan SL, Viera AJ, Krantz MJ, et al. The effect of giving global coronary risk information to adults: a systematic review. Arch Intern Med Feb ;170(3): Sheridan SL, Crespo E. Does the routine use of global coronary heart disease risk scores translate into clinical benefits or harms? A systematic review of the literature. BMC Health Serv Res ;8: Murray E, Pollack L, White M, Lo B. Clinical decision-making: physicians' preferences and experiences. BMC Fam Pract ;8: Shillinglaw B, Viera AJ, Edwards T, Simpson R, Sheridan SL. Use of global coronary heart disease risk assessment in practice: a cross-sectional survey of a sample of U.S. physicians. BMC Health Serv Res. 2012;12: Hobbs FD, Erhardt L. Acceptance of guideline recommendations and perceived implementation of coronary heart disease prevention among primary care physicians in five European countries: the Reassessing European Attitudes about Cardiovascular Treatment (REACT) survey. Fam Pract. Dec 2002;19(6): Sposito AC, Ramires JA, Jukema JW, et al. Physicians' attitudes and adherence to use of risk scores for primary prevention of cardiovascular disease: cross-sectional survey in three world regions. Curr Med Res Opin. May 2009;25(5): De Muylder R, Lorant V, Paulus D, Nackers F, Jeanjean M, Boland B. Obstacles to cardiovascular prevention in general practice. Acta cardiologica. Apr 2004;59(2): Berner ES. Clinical Decision Support Systems: State of the Art. Rockville, MD: Agency for Healthcare Research and Quality; Karsh B-T. Clinical practice improvement and redesign: how change in workflow can be supported by clinical decision support. Rockville, MD: Agency for Healthcare Research and Quality; Stone EG, Morton SC, Hulscher ME, et al. Interventions that increase use of adult immunization and cancer screening services: a meta-analysis. Ann Intern Med. May ;136(9): Sheridan SL, Draeger, L.B., Pignone, M.P., Samuel-Hodge, C., Tracy, R., Keyserling, T.K. A randomized comparative effectiveness trial of two strategies for CHD prevention: Methods and baseline results from the Heart to Health study. [Under preparation]

7 19. Sheridan SL, Draeger LB, Pignone MP, et al. A randomized trial of an intervention to improve use and adherence to effective coronary heart disease prevention strategies. BMC Health Serv Res. 2011;11: Sheridan SL, Shadle J, Simpson RJJ, Pignone MP. The impact of a decision aid about heart disease prevention on patients' discussions with their doctor and their plans for prevention: a pilot randomized trial. BMC Health Serv Res ;6: Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation May ;97(18): Wolf PA, D'Agostino RB, Belanger AJ, Kannel WB. Probability of stroke: a risk profile from the Framingham Study. Stroke; a journal of cerebral circulation. Mar 1991;22(3): D'Agostino RB, Wolf PA, Belanger AJ, Kannel WB. Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study. Stroke; a journal of cerebral circulation. Jan 1994;25(1): Karsh BT, Holden RJ, Alper SJ, Or CK. A human factors engineering paradigm for patient safety: designing to support the performance of the healthcare professional. Qual Saf Health Care. Dec 2006;15 Suppl 1:i59-65.